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	Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling
	Zichang Tan1,2; Shuai Zhou1,3; Jun Wan1,2; Zhen Lei1,2; Stan Z. Li1,2
	2016
会议日期	November 20-24, 2016
会议地点	Taipei, Taiwan
英文摘要	In this paper, we propose a novel approach based on a single convolutional neural network (CNN) for age estimation. In our proposed network architecture, we first model the randomness of aging with the Gaussian distribution which is used to calculate the Gaussian integral of an age interval. Then, we present a soft softmax regression function used in the network. The new function applies the aging modeling to compute the function loss. Compared with the traditional softmax function, the new function considers not only the chronological age but also the interval nearby true age. Moreover, owing to the complex of Gaussian integral in soft softmax function, a look up table is built to accelerate this process. All the integrals of age values are calculated offline in advance. We evaluate our method on two public datasets: MORPH II and Cross-Age Celebrity Dataset (CACD), and experimental results have shown that the proposed method has gained superior performances compared to the state of the art.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/15298]
专题	自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者	Jun Wan
作者单位	1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences 3.Faculty of Information Technology, Macau University of Science and Technology, Macau
推荐引用方式 GB/T 7714	Zichang Tan,Shuai Zhou,Jun Wan,et al. Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling[C]. 见:. Taipei, Taiwan. November 20-24, 2016.

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